code: def _map_key(key):
      key_ = key
      if key.startswith("w2v_model."):
          key = key.replace("w2v_model.", "")
      if re.match(
          r"(mask_emb|quantizer|project_q|final_proj|mask_emb)", key
      ):
          return None
      # Feature Extractor
      # Group norm when "extractor_mode" is "default".
      # (Only the first layer)
      # "conv_layers.0.2.weight" -> "conv_layers.0.layer_norm.weight"
      # "conv_layers.0.2.bias"   -> "conv_layers.0.layer_norm.bias"
      match = re.match(
          r"feature_extractor\.conv_layers\.0\.2\.(weight|bias)", key
      )
      if match:
          return f"feature_extractor.conv_layers.0.layer_norm.{match.group(1)}"
      # Convolutions
      # "conv_layers.X.0.weight" -> "conv_layers.X.conv.weight"
      # "conv_layers.X.0.bias"   -> "conv_layers.X.conv.bias"
      match = re.match(
          r"feature_extractor\.conv_layers\.(\d+)\.0\.(weight|bias)",
          key,
      )
      if match:
          return f"feature_extractor.conv_layers.{match.group(1)}.conv.{match.group(2)}"
      # Layer norm when "extractor_mode" is "layer_norm".
      # "conv_layers.X.2.1.weight" -> "conv_layers.X.layer_norm.weight"
      # "conv_layers.X.2.1.bias"   -> "conv_layers.X.layer_norm.bias"
      match = re.match(
          r"feature_extractor\.conv_layers\.(\d+)\.2\.1\.(weight|bias)",
          key,
      )
      if match:
          return f"feature_extractor.conv_layers.{match.group(1)}.layer_norm.{match.group(2)}"
      match = re.match(r"post_extract_proj\.(weight|bias)", key)
      # Encoder - Feature projection
      if match:
          return (
              f"encoder.feature_projection.projection.{match.group(1)}"
          )
      match = re.match(r"layer_norm\.(weight|bias)", key)
      if match:
          return (
              f"encoder.feature_projection.layer_norm.{match.group(1)}"
          )
      # Encoder - Transformer - Convolutional positional embedding
      match = re.match(
          r"encoder\.pos_conv\.0\.(bias|weight_g|weight_v)", key
      )
      if match:
          return f"encoder.transformer.pos_conv_embed.conv.{match.group(1)}"
      match = re.match(r"encoder\.layer_norm\.(weight|bias)", key)
      if match:
          return f"encoder.transformer.layer_norm.{match.group(1)}"
      # Encoder - Transformer - Self attention layers
      match = re.match(
          r"encoder\.layers\.(\d+)\.self_attn\.((k_|v_|q_|out_)proj\.(weight|bias))",
          key,
      )
      if match:
          return f"encoder.transformer.layers.{match.group(1)}.attention.{match.group(2)}"
      match = re.match(
          r"encoder\.layers\.(\d+)\.self_attn_layer_norm\.(weight|bias)",
          key,
      )
      if match:
          return f"encoder.transformer.layers.{match.group(1)}.layer_norm.{match.group(2)}"
      match = re.match(
          r"encoder\.layers\.(\d+)\.fc1\.(weight|bias)", key
      )
      if match:
          return f"encoder.transformer.layers.{match.group(1)}.feed_forward.intermediate_dense.{match.group(2)}"
      match = re.match(
          r"encoder\.layers\.(\d+)\.fc2\.(weight|bias)", key
      )
      if match:
          return f"encoder.transformer.layers.{match.group(1)}.feed_forward.output_dense.{match.group(2)}"
      match = re.match(
          r"encoder\.layers\.(\d+)\.final_layer_norm\.(weight|bias)",
          key,
      )
      if match:
          return f"encoder.transformer.layers.{match.group(1)}.final_layer_norm.{match.group(2)}"
      match = re.match(r"proj\.(weight|bias)", key)
      # Auxiliary Module
      # Only relevant when loading fine-tuned models
      if match:
          return f"aux.{match.group(1)}"
      # HuBERT Extension
      if key in ["label_embs_concat"]:
          return key
      raise ValueError(f"Unexpected key: {key_}")
  
file_path: audio/src/torchaudio/models/wav2vec2/utils/import_fairseq.py
imports: import re
  from torch.nn import Module
  from ..model import wav2vec2_model, Wav2Vec2Model
label_desc: Use image node_id 2 for calling a global function with example usage: _map_key(key) and returns: None, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, str, match, key